class_docstr: Reference: https://docs.nlpcloud.com/#generation
  
  - `max_length` (int): Optional. The maximum number of tokens that the generated text should contain.
  
  - `length_no_input` (boolean): Optional. Whether `min_length` and `max_length` should not include the length of the input text.
  
  - `end_sequence` (string): Optional. A specific token that should be the end of the generated sequence.
  
  - `remove_end_sequence` (boolean): Optional. Whether to remove the `end_sequence` string from the result.
  
  - `remove_input` (boolean): Optional. Whether to remove the input text from the result.
  
  - `bad_words` (list of strings): Optional. List of tokens that are not allowed to be generated.
  
  - `temperature` (float): Optional. Temperature sampling. It modulates the next token probabilities.
  
  - `top_p` (float): Optional. Top P sampling. Below 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.
  
  - `top_k` (int): Optional. Top K sampling. The number of highest probability vocabulary tokens to keep for top k filtering.
  
  - `repetition_penalty` (float): Optional. Prevents the same word from being repeated too many times.
  
  - `num_beams` (int): Optional. Number of beams for beam search.
  
  - `num_return_sequences` (int): Optional. The number of independently computed returned sequences.
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code: def __init__(
      self,
      max_length: Optional[int] = None,
      length_no_input: Optional[bool] = None,
      end_sequence: Optional[str] = None,
      remove_end_sequence: Optional[bool] = None,
      remove_input: Optional[bool] = None,
      bad_words: Optional[list] = None,
      temperature: Optional[float] = None,
      top_p: Optional[float] = None,
      top_k: Optional[int] = None,
      repetition_penalty: Optional[float] = None,
      num_beams: Optional[int] = None,
      num_return_sequences: Optional[int] = None,
  ) -> None:
      locals_ = locals()
      for key, value in locals_.items():
          if key != "self" and value is not None:
              setattr(self.__class__, key, value)
  
file_path: litellm/litellm/llms/nlp_cloud.py
imports: import os, types
  import json
  from enum import Enum
  import requests
  import time
  from typing import Callable, Optional
  import litellm
  from litellm.utils import ModelResponse, Usage
label_desc: Use image node_id 1 to create a new NLPCloudConfig object  with example: obj = NLPCloudConfig(max_length, length_no_input, end_sequence, remove_end_sequence, remove_input, bad_words, temperature, top_p, top_k, repetition_penalty, num_beams, num_return_sequences)